void hup_handler(int sig)
{
double accuracy = calculate_accuracy(total_count, total_time);
printf("\n--- Results after %d passes ---\n", pass);
printf("Best: %.3f%% -- Worst: %.3f%% -- Average: %f%%\n",
best, worst, pass ? total/pass : 100.00);
printf("Cumulative Accuracy (not per pass): %0.3f\n",
pass ? accuracy : 0.0);
exit(0);
}
int main(int argc, char **argv[])
{
string name;
vector<Mat>Images(100), TestImages(50);
vector<Mat> Descriptor(100), TestDescriptor(50), TestPcafeature(50);
vector<vector<KeyPoint>>Keypoints(100), TestKeypoint(50);
Mat histogram = Mat::zeros(100, Cluster, CV_32F);
Mat Testhistogram = Mat::zeros(50, Cluster, CV_32F);
Mat Keyword = Mat::zeros(Cluster, 20, CV_32F);
Mat full_Descriptor, Pcafeature, Pcaduplicate, clusteridx, trainlabels(100, 1, CV_32F);
vector<vector<DMatch>> matches(50);
Mat predicted(Testhistogram.rows, 1, CV_32F);
// Read Training Images.
read_train(Images, name);
//Calculate SIFT features for the Training Images.
calculate_SIFT(Images,Keypoints,Descriptor);
merge_descriptor(full_Descriptor,Descriptor);
//Compute PCA for all the features across all Images.
PCA pca;
perform_PCA(full_Descriptor, Pcafeature, pca);
//Perform K-Means on all the PCA reduced features.
Pcafeature.convertTo(Pcaduplicate, CV_32F);
calculate_Kmeans(Pcaduplicate, clusteridx);
//Calculate the Keywords in the Feature Space.
make_dictionary(clusteridx, Pcaduplicate, Keyword);
//Get the Histogram for each Training Image.
hist(Descriptor, clusteridx, histogram);
//Read Test Image
read_test(TestImages, name);
//Calculate the SIFT feature for all the test Images.
calculate_SIFT(TestImages, TestKeypoint, TestDescriptor);
//Project the SIFT feature of each feature on the lower dimensional PCA plane calculated above.
pca_testProject(TestDescriptor, TestPcafeature, pca);
//Find the Label by searching for keywords closest to current feature.
get_matches(TestPcafeature,Keyword,matches);
//Calculate Histogram for each test Image.
hist_test(TestDescriptor, matches, Testhistogram);
//Perform classification through Knn Classifier.
train_labels(trainlabels);
KNearest knn;
train_classifier(histogram, trainlabels, knn);
test_classify(Testhistogram,predicted,knn);
//Calculate Accuracy for each class.
calculate_accuracy(predicted);
getchar();
return 0;
}
int main(int argc, char *argv[])
{
int fd;
int res;
int c;
int count = 0;
int seconds = 0;
int curarg = 1;
char buf[8192];
float ms;
struct timeval start, now;
fd = open("/dev/dahdi/pseudo", O_RDWR);
if (fd < 0) {
fprintf(stderr, "Unable to open dahdi interface: %s\n", strerror(errno));
exit(1);
}
while ((c = getopt(argc, argv, "c:hv")) != -1) {
switch(c) {
case 'c':
seconds = atoi(optarg);
break;
case 'h':
usage(argv[0]);
exit(0);
break;
case '?':
usage(argv[0]);
exit(1);
break;
case 'v':
verbose++;
break;
}
}
while (curarg < argc) {
if (!strcasecmp(argv[curarg], "-v"))
verbose++;
if (!strcasecmp(argv[curarg], "-c") && argc > curarg)
seconds = atoi(argv[curarg + 1]);
curarg++;
}
printf("Opened pseudo dahdi interface, measuring accuracy...\n");
signal(SIGHUP, hup_handler);
signal(SIGINT, hup_handler);
signal(SIGALRM, hup_handler);
/* Flush input buffer */
for (count = 0; count < 4; count++)
res = read(fd, buf, sizeof(buf));
count = 0;
ms = 0; /* Makes the compiler happy */
if (seconds > 0)
alarm(seconds + 1); /* This will give 'seconds' cycles */
for (;;) {
if (count == 0)
ms = 0;
gettimeofday(&start, NULL);
res = read(fd, buf, sizeof(buf));
if (res < 0) {
fprintf(stderr, "Failed to read from pseudo interface: %s\n", strerror(errno));
exit(1);
}
count += res;
gettimeofday(&now, NULL);
ms += (now.tv_sec - start.tv_sec) * 8000;
ms += (now.tv_usec - start.tv_usec) / 125.0;
if (count >= SIZE) {
const double percent = calculate_accuracy(count, ms);
if (verbose) {
printf("\n%d samples in %0.3f system clock sample intervals (%.3f%%)",
count, ms, percent);
} else if (pass > 0 && (pass % 8) == 0) {
printf("\n");
}
if (percent > best)
best = percent;
if (percent < worst)
worst = percent;
if (!verbose)
printf("%.3f%% ", percent);
total += percent;
fflush(stdout);
total_count += count;
total_time += ms;
count = 0;
pass++;
}
}
}
